Achieving low deployment cost without sacrificing performance gains requires an unique workflow to port machine learning models onto microcontrollers compared to traditional model design. Fig. REF  illustrates the general "closed-loop" workflow for TinyML model development and deployment. For various parts of this workflow, specific technologies and variations have emerged [1]}[2]}[3]}, which we discuss in upcoming sections. The workflow can be divided into two phases:
